import math

import torch
import torch.nn as nn
import torch.nn.functional as F


def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
    '''
    return a tuple of two value of input dimension decomposed by the number closest to factor
    second value is higher or equal than first value.
    
    In LoRA with Kroneckor Product, first value is a value for weight scale.
    secon value is a value for weight.
    
    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
    
    examples)
    factor
        -1               2                4               8               16               ...
    127 -> 127, 1   127 -> 127, 1    127 -> 127, 1   127 -> 127, 1   127 -> 127, 1
    128 -> 16, 8    128 -> 64, 2     128 -> 32, 4    128 -> 16, 8    128 -> 16, 8
    250 -> 25, 10   250 -> 125, 2    250 -> 125, 2   250 -> 50, 5   250 -> 25, 10
    360 -> 45, 8    360 -> 180, 2    360 -> 90, 4    360 -> 45, 8    360 -> 45, 8
    512 -> 32, 16   512 -> 256, 2    512 -> 128, 4   512 -> 64, 8    512 -> 32, 16
    1024 -> 32, 32  1024 -> 512, 2   1024 -> 256, 4  1024 -> 128, 8  1024 -> 64, 16
    '''
    
    if factor > 0 and (dimension % factor) == 0:
        m = factor
        n = dimension // factor
        return m, n
    if factor == -1:
        factor = dimension
    m, n = 1, dimension
    length = m + n
    while m<n:
        new_m = m + 1
        while dimension%new_m != 0:
            new_m += 1
        new_n = dimension // new_m
        if new_m + new_n > length or new_m>factor:
            break
        else:
            m, n = new_m, new_n
    if m > n:
        n, m = m, n
    return m, n


def make_weight_cp(t, wa, wb):
    rebuild2 = torch.einsum('i j k l, i p, j r -> p r k l', t, wa, wb) # [c, d, k1, k2]
    return rebuild2


def make_kron(w1, w2, scale):
    if len(w2.shape) == 4:
        w1 = w1.unsqueeze(2).unsqueeze(2)
    w2 = w2.contiguous()
    rebuild = torch.kron(w1, w2)
    
    return rebuild*scale


class LokrModule(nn.Module):
    """
    modifed from kohya-ss/sd-scripts/networks/lora:LoRAModule
        and from KohakuBlueleaf/LyCORIS/lycoris:loha:LoHaModule
        and from KohakuBlueleaf/LyCORIS/lycoris:locon:LoconModule
    """

    def __init__(
        self, 
        lora_name, org_module: nn.Module, 
        multiplier=1.0, 
        lora_dim=4, alpha=1, 
        dropout=0., rank_dropout=0., module_dropout=0.,
        use_tucker=False,
        decompose_both = False,
        factor:int=-1, # factorization factor
        **kwargs,
    ):
        """ if alpha == 0 or None, alpha is rank (no scaling). """
        super().__init__()
        factor = int(factor)
        self.lora_name = lora_name
        self.lora_dim = lora_dim
        self.tucker = False
        self.use_w1 = False
        self.use_w2 = False

        self.shape = org_module.weight.shape
        if org_module.__class__.__name__ == 'Conv2d':
            in_dim = org_module.in_channels
            k_size = org_module.kernel_size
            out_dim = org_module.out_channels
            
            in_m, in_n = factorization(in_dim, factor)
            out_l, out_k = factorization(out_dim, factor)
            shape = ((out_l, out_k), (in_m, in_n), *k_size) # ((a, b), (c, d), *k_size)
            
            self.tucker = use_tucker and k_size!=(1, 1)
            if decompose_both and lora_dim < max(shape[0][0], shape[1][0])/2:
                self.lokr_w1_a = nn.Parameter(torch.empty(shape[0][0], lora_dim))
                self.lokr_w1_b = nn.Parameter(torch.empty(lora_dim, shape[1][0]))
            else:
                self.use_w1 = True
                self.lokr_w1 = nn.Parameter(torch.empty(shape[0][0], shape[1][0]))  # a*c, 1-mode
            
            if lora_dim >= max(shape[0][1], shape[1][1])/2:
                self.use_w2 = True
                self.lokr_w2 = nn.Parameter(torch.empty(shape[0][1], shape[1][1], *k_size))
            elif self.tucker:
                self.lokr_t2 = nn.Parameter(torch.empty(lora_dim, lora_dim, shape[2], shape[3]))
                self.lokr_w2_a = nn.Parameter(torch.empty(lora_dim, shape[0][1])) # b, 1-mode
                self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, shape[1][1])) # d, 2-mode
            else: # Conv2d not cp
                # bigger part. weight and LoRA. [b, dim] x [dim, d*k1*k2]
                self.lokr_w2_a = nn.Parameter(torch.empty(shape[0][1], lora_dim))
                self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, shape[1][1]*shape[2]*shape[3]))
                # w1 ⊗ (w2_a x w2_b) = (a, b)⊗((c, dim)x(dim, d*k1*k2)) = (a, b)⊗(c, d*k1*k2) = (ac, bd*k1*k2)
            
            self.op = F.conv2d
            self.extra_args = {
                "stride": org_module.stride,
                "padding": org_module.padding,
                "dilation": org_module.dilation,
                "groups": org_module.groups
            }

        else: # Linear
            in_dim = org_module.in_features
            out_dim = org_module.out_features
            
            in_m, in_n = factorization(in_dim, factor)
            out_l, out_k = factorization(out_dim, factor)
            shape = ((out_l, out_k), (in_m, in_n)) # ((a, b), (c, d)), out_dim = a*c, in_dim = b*d
            
            # smaller part. weight scale
            if decompose_both and lora_dim < max(shape[0][0], shape[1][0])/2:
                self.lokr_w1_a = nn.Parameter(torch.empty(shape[0][0], lora_dim))
                self.lokr_w1_b = nn.Parameter(torch.empty(lora_dim, shape[1][0]))
            else:
                self.use_w1 = True
                self.lokr_w1 = nn.Parameter(torch.empty(shape[0][0], shape[1][0]))  # a*c, 1-mode

            if lora_dim < max(shape[0][1], shape[1][1])/2:
                # bigger part. weight and LoRA. [b, dim] x [dim, d]
                self.lokr_w2_a = nn.Parameter(torch.empty(shape[0][1], lora_dim))
                self.lokr_w2_b = nn.Parameter(torch.empty(lora_dim, shape[1][1]))
                # w1 ⊗ (w2_a x w2_b) = (a, b)⊗((c, dim)x(dim, d)) = (a, b)⊗(c, d) = (ac, bd)
            else:
                self.use_w2 = True
                self.lokr_w2 = nn.Parameter(torch.empty(shape[0][1], shape[1][1]))

            self.op = F.linear
            self.extra_args = {}
        
        self.dropout = dropout
        if dropout:
            print("[WARN]LoHa/LoKr haven't implemented normal dropout yet.")
        self.rank_dropout = rank_dropout
        self.module_dropout = module_dropout
        
        if isinstance(alpha, torch.Tensor):
            alpha = alpha.detach().float().numpy()  # without casting, bf16 causes error
        alpha = lora_dim if alpha is None or alpha == 0 else alpha
        if self.use_w2 and self.use_w1:
            #use scale = 1
            alpha = lora_dim
        self.scale = alpha / self.lora_dim
        self.register_buffer('alpha', torch.tensor(alpha)) # 定数として扱える

        if self.use_w2:
            torch.nn.init.constant_(self.lokr_w2, 0)
        else:
            if self.tucker:
                torch.nn.init.normal_(self.lokr_t2, std=0.1)
            torch.nn.init.normal_(self.lokr_w2_a, std=1)
            torch.nn.init.constant_(self.lokr_w2_b, 0)
        
        if self.use_w1:
            torch.nn.init.normal_(self.lokr_w1, std=1)
        else:
            torch.nn.init.normal_(self.lokr_w1_a, std=1)
            torch.nn.init.normal_(self.lokr_w1_b, std=0.1)

        self.multiplier = multiplier
        self.org_module = [org_module]
        weight = make_kron(
            self.lokr_w1 if self.use_w1 else self.lokr_w1_a@self.lokr_w1_b,
            (self.lokr_w2 if self.use_w2 
             else make_weight_cp(self.lokr_t2, self.lokr_w2_a, self.lokr_w2_b) if self.tucker 
             else self.lokr_w2_a@self.lokr_w2_b),
            torch.tensor(self.multiplier * self.scale)
        )
        assert torch.sum(torch.isnan(weight)) == 0, "weight is nan"

    # Same as locon.py
    def apply_to(self):
        self.org_forward = self.org_module[0].forward
        self.org_module[0].forward = self.forward
    
    def get_weight(self, orig_weight = None):
        weight = make_kron(
            self.lokr_w1 if self.use_w1 else self.lokr_w1_a@self.lokr_w1_b,
            (self.lokr_w2 if self.use_w2 
             else make_weight_cp(self.lokr_t2, self.lokr_w2_a, self.lokr_w2_b) if self.tucker 
             else self.lokr_w2_a@self.lokr_w2_b),
            torch.tensor(self.scale)
        )
        if orig_weight is not None:
            weight = weight.reshape(orig_weight.shape)
        if self.training and self.rank_dropout:
            drop = torch.rand(weight.size(0)) < self.rank_dropout
            weight *= drop.view(-1, [1]*len(weight.shape[1:])).to(weight.device)
        return weight

    @torch.no_grad()
    def apply_max_norm(self, max_norm, device=None):
        orig_norm = self.get_weight().norm()
        norm = torch.clamp(orig_norm, max_norm/2)
        desired = torch.clamp(norm, max=max_norm)
        ratio = desired.cpu()/norm.cpu()
        
        scaled = ratio.item() != 1.0
        if scaled:
            modules = (4 - self.use_w1 - self.use_w2 + (not self.use_w2 and self.tucker))
            if self.use_w1:
                self.lokr_w1 *= ratio**(1/modules)
            else:
                self.lokr_w1_a *= ratio**(1/modules)
                self.lokr_w1_b *= ratio**(1/modules)
            
            if self.use_w2:
                self.lokr_w2 *= ratio**(1/modules)
            else:
                if self.tucker:
                    self.lokr_t2 *= ratio**(1/modules)
                self.lokr_w2_a  *= ratio**(1/modules)
                self.lokr_w2_b  *= ratio**(1/modules)
        
        return scaled, orig_norm*ratio

    def forward(self, x):
        if self.module_dropout and self.training:
            if torch.rand(1) < self.module_dropout:
                return self.op(
                    x,
                    self.org_module[0].weight.data,
                    None if self.org_module[0].bias is None else self.org_module[0].bias.data
                )
        weight = (
            self.org_module[0].weight.data 
            + self.get_weight(self.org_module[0].weight.data) * self.multiplier
        )
        bias = None if self.org_module[0].bias is None else self.org_module[0].bias.data
        return self.op(
            x, 
            weight.view(self.shape),
            bias,
            **self.extra_args
        )
